Measuring Venezuela’s Economy from Space Using Satellites and Machine Learning

IMF researchers show that satellite data, especially nighttime lights combined with machine learning can reliably estimate Venezuela’s economic growth even after official GDP data stopped being published. The study demonstrates that modern tools can track economic activity in data-scarce, crisis-hit countries when traditional statistics fail.

When governments stop publishing economic data, the effects go far beyond economists’ spreadsheets. Investors lose confidence, policymakers guess in the dark, and ordinary citizens have no clear sense of whether life is getting better or worse. This is exactly what has happened in Venezuela, where official figures on economic growth have largely disappeared since early 2019. With no reliable GDP data, even basic questions about recovery or decline became impossible to answer.

A new study by researchers from the International Monetary Fund, working with an academic collaborator from Heidelberg University, explores a striking alternative: measuring economic growth from space. By combining satellite imagery with machine learning, the researchers show that it is possible to estimate Venezuela’s economic performance even when official statistics fall silent.

Venezuela’s Economic Blind Spot

Venezuela’s data blackout came amid one of the deepest economic collapses in modern history. After years of dependence on oil revenues, the economy began shrinking sharply after 2013. By 2020, real GDP had fallen by about three-quarters. Hyperinflation wiped out incomes and savings, infrastructure deteriorated, and millions of people left the country. Although growth returned after 2021, the economy in 2024 was still far smaller than it had been a decade earlier.

Traditional economic forecasting tools struggle in such conditions. They depend on regular, reliable data and stable relationships between variables, assumptions that simply do not hold in a country affected by sanctions, policy shocks, and institutional breakdown. Faced with this reality, the researchers looked for new ways to observe economic activity indirectly.

What Satellites Can See

Satellites orbiting Earth continuously collect information about human activity. The study uses three main types of satellite data. The first is nighttime light intensity, which captures how brightly cities, roads, and industrial areas glow after dark. Brighter lights usually mean more electricity use, more businesses operating, and more economic activity. The second is vegetation data, which tracks changes in farmland, forests, and land use, offering clues about agriculture and development. The third is nitrogen dioxide emissions, a pollutant linked to vehicle emissions, industrial activity, and power generation.

None of these indicators was designed to measure GDP. But together, they provide a real-time picture of an economy's activity, without relying on government reporting. These satellite signals were combined with whatever traditional data could still be obtained from alternative sources, such as oil production, gas consumption, tax revenues, credit, and industrial capacity.

Teaching Machines to Read the Economy

To turn this unusual mix of data into GDP estimates, the researchers tested different models. One was a standard economic tool widely used by central banks. The other was a machine learning method called a Random Forest, which builds many decision trees and averages their results. This approach is especially good at handling messy data, sudden changes, and complex relationships.

The difference was clear. The traditional model struggled in Venezuela’s unstable environment. The machine learning model performed much better, especially when satellite data were included. Forecasting errors fell sharply, and the model captured economic swings more accurately. Among all the new indicators, nighttime lights proved especially powerful, ranking alongside oil production as one of the strongest signals of economic growth.

Simply put, how brightly Venezuela shines at night turned out to be a surprisingly reliable measure of how its economy is doing.

Why This Matters Beyond Venezuela

The researchers are careful to stress that satellite data and machine learning are not a replacement for official statistics. Satellite images can be distorted, for example, gas flaring in oil fields can make areas look brighter without reflecting broader prosperity. That is why combining several indicators is essential.

Still, the implications are far-reaching. Many countries affected by conflict, fragility, or political instability suffer from serious data gaps. Satellite data are public, frequent, and hard to manipulate. When paired with modern analytical tools, they offer a way to track economic trends, detect turning points, and inform policy when traditional numbers are missing.

In a world where economic crises often go hand in hand with information breakdowns, the study sends a powerful message: even when governments stop publishing statistics, economies do not become invisible. They still leave traces on roads, in cities, and in the glow of lights seen from space.